Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method comprising, by an interference-management system of a multi-hop wireless network: accessing an interference map indicating interference among network nodes of the multi-hop wireless network, wherein each of the network nodes of the multi-hop wireless network comprises one or more sectors that each comprise an array of beamforming antennae; identifying one or more links between network nodes of the multi-hop wireless network, wherein each link is associated with a transmitting (TX) beamforming weight variable and a receiving (RX) beamforming weight variable; generating a factor-graph representation of the multi-hop wireless network, wherein the factor-graph representation comprises a first set of vertices corresponding to a second set of vertices, each vertex pair being associated with an identified link, and wherein, for each vertex pair, the vertex in the first set is a variable node representing the pair of beamforming weight variables associated with the identified link and each vertex in the second set is a function node representing a capacity equation associated with the identified link; and determining, for at least a first identified link, one or more adjustments to one or more beamforming weights associated with the first identified link to reduce interference among the network nodes of the multi-hop wireless network, wherein the one or more adjustments are determined based at least in part on one or more messages passed from a first variable node representing the pair of beamforming weight variables associated with the first identified link to a first function node representing a capacity equation associated with the first identified link, and wherein the one or more messages are derived at least in part based on one or more messages previously passed from the first variable node to the first function node.
This invention relates to interference management in multi-hop wireless networks, particularly those with beamforming antenna arrays. The problem addressed is reducing interference among network nodes, each of which may have multiple sectors with beamforming antenna arrays. The method involves accessing an interference map that details interference patterns among nodes. It identifies links between nodes, where each link has associated transmitting (TX) and receiving (RX) beamforming weight variables. A factor-graph representation of the network is generated, where vertices in a first set correspond to vertices in a second set, forming pairs linked by identified network links. Each pair consists of a variable node representing the beamforming weight variables for a link and a function node representing a capacity equation for that link. The system then determines adjustments to beamforming weights for at least one link to reduce interference. These adjustments are based on messages passed between variable nodes and function nodes in the factor graph, where messages are derived from prior message exchanges. The approach leverages iterative message passing to optimize beamforming weights while accounting for interference constraints.
2. The method of claim 1 , wherein each variable node further represents a signal phase shift variable associated with the identified link.
A system and method for optimizing signal transmission in a communication network involves analyzing and adjusting signal phase shifts to improve performance. The invention addresses the challenge of signal degradation and interference in multi-link communication environments, where phase misalignment between transmitted signals can reduce data integrity and throughput. The method includes identifying a communication link between nodes in the network and determining a phase shift variable associated with that link. Each variable node in the system represents a signal phase shift, which is adjusted to compensate for phase distortions caused by environmental factors or hardware limitations. By dynamically modifying these phase shifts, the system ensures that signals arrive at their destination with minimal phase errors, enhancing synchronization and reducing errors. The method may also involve iterative adjustments based on feedback from received signals to further refine phase alignment. This approach is particularly useful in high-frequency or high-density networks where precise phase control is critical for maintaining signal quality. The invention improves network reliability and efficiency by mitigating phase-related signal degradation, leading to better overall performance in data transmission.
3. The method of claim 1 , wherein each variable node further represents a time division multiplexing variable associated with the identified link.
A method for managing communication links in a network involves assigning variable nodes to represent time division multiplexing (TDM) variables associated with identified links. These variable nodes are part of a network topology that includes multiple nodes and links, where each link is identified by a unique identifier. The method further involves determining a set of constraints for the network, where these constraints are derived from physical layer parameters such as bandwidth, latency, and reliability requirements. The constraints are then used to optimize the network topology, ensuring that the TDM variables associated with each link are properly configured to meet the specified performance criteria. The optimization process may involve adjusting the TDM variables to balance load distribution, minimize latency, or enhance reliability across the network. The method ensures that the network operates efficiently by dynamically adapting the TDM variables based on real-time conditions and predefined constraints, thereby improving overall communication performance.
4. The method of claim 1 , wherein one or more of the identified links are golden links.
A system and method for identifying and utilizing golden links in a networked environment. The technology addresses the challenge of efficiently managing and prioritizing connections or links within a network, particularly in scenarios where certain links (referred to as golden links) exhibit superior performance, reliability, or other desirable characteristics. Golden links may be defined by criteria such as low latency, high bandwidth, or secure communication channels, and their identification is crucial for optimizing network performance, reducing latency, and ensuring reliable data transmission. The method involves analyzing network topology and performance metrics to detect and classify links as golden links based on predefined criteria. Once identified, these golden links are prioritized for critical data transmission, load balancing, or failover mechanisms. The system may also dynamically adjust routing or resource allocation to favor golden links, improving overall network efficiency. Additionally, the method may include monitoring and updating the classification of golden links in real-time to adapt to changing network conditions. This approach ensures that the most reliable and high-performance links are consistently utilized, enhancing network robustness and user experience.
5. The method of claim 4 , wherein the one or more golden links are identified based on a proximity to a vital network node.
This invention relates to network security, specifically identifying critical network links to enhance threat detection and response. The method involves analyzing network topology to determine one or more "golden links" that are prioritized for monitoring due to their strategic importance. These links are identified based on their proximity to vital network nodes, which are key infrastructure points such as servers, routers, or data centers that are critical to network operations. By focusing on these high-value links, the system can detect and mitigate threats more effectively, reducing the risk of network disruptions or data breaches. The method may also involve dynamically adjusting the monitoring of these links based on real-time network conditions or threat intelligence. This approach improves security by concentrating resources on the most vulnerable or critical parts of the network, rather than applying uniform monitoring across all connections. The invention is particularly useful in large-scale or complex networks where identifying and protecting key infrastructure is essential for maintaining operational integrity.
6. The method of claim 5 , wherein the vital network node connects the multi-hop wireless network to a backbone network.
A method for managing network connectivity in a multi-hop wireless network involves a vital network node that serves as a bridge between the multi-hop wireless network and a backbone network. The backbone network provides broader connectivity, such as internet access or interconnection with other networks. The vital network node ensures reliable communication by maintaining a stable link to the backbone network while facilitating data routing within the multi-hop wireless network. This node may employ adaptive routing protocols to optimize data flow, prioritize critical traffic, or dynamically adjust connections based on network conditions. The method may also include monitoring the vital node's status to detect failures or performance degradation, triggering failover mechanisms if necessary. By integrating the vital node as a critical link, the system enhances overall network resilience and ensures continuous connectivity between the multi-hop wireless network and external infrastructure. This approach is particularly useful in scenarios where the multi-hop network operates in remote or challenging environments where direct backbone access is limited.
7. The method of claim 1 , wherein a set of possible values for each variable node is identified based on the one or more previously passed messages.
A system and method for optimizing variable node processing in a belief propagation algorithm, particularly for error correction in communication systems. The invention addresses the computational inefficiency in belief propagation when processing variable nodes, which can lead to excessive processing time and resource consumption. The method involves identifying a set of possible values for each variable node based on previously passed messages, rather than evaluating all possible values. This selective evaluation reduces the computational load by focusing only on relevant values, improving processing speed and efficiency. The approach leverages message-passing between nodes in a graph-based structure, where each message represents probabilistic information exchanged between variable and check nodes. By narrowing down the possible values for each variable node using prior message data, the system avoids unnecessary calculations, enhancing overall performance. The method is particularly useful in low-density parity-check (LDPC) codes and other iterative decoding algorithms where belief propagation is applied. The invention ensures faster convergence and lower resource usage while maintaining accuracy in error correction.
8. The method of claim 7 , wherein the set of possible values for each variable node corresponds to a set of micro-routes and nano-routes on the multi-hop wireless network.
This invention relates to optimizing routing in multi-hop wireless networks by associating variable nodes in a network model with specific micro-routes and nano-routes. The technology addresses the challenge of efficiently determining optimal paths in dynamic wireless environments where traditional routing methods may fail due to node mobility, interference, or limited bandwidth. The method involves constructing a network model where variable nodes represent potential routing choices, and each node is assigned a set of possible values corresponding to micro-routes (short-range, high-frequency connections) and nano-routes (ultra-short-range, low-power connections). By evaluating these routes, the system selects the most efficient path based on factors like latency, energy consumption, and reliability. The approach improves network performance by dynamically adapting to changing conditions, ensuring robust connectivity in environments with high node density or frequent topology changes. The solution is particularly useful in IoT, sensor networks, and ad-hoc wireless systems where traditional routing protocols struggle with scalability and efficiency.
9. The method of claim 8 , wherein the amount of possible values for each variable node is two, three, or four.
A system and method for optimizing variable node values in a computational framework, particularly in error correction or decoding algorithms, addresses the challenge of efficiently managing variable nodes with limited possible values. The method involves processing a graph-based structure where variable nodes represent data elements and constraint nodes enforce relationships between them. The key innovation lies in restricting the number of possible values for each variable node to a small, finite set—specifically two, three, or four values. This reduction simplifies computations, improves processing speed, and minimizes resource usage while maintaining accuracy. The method may include iterative updates to variable node values based on constraints, ensuring consistency across the graph. By limiting the value set, the system avoids excessive computational overhead associated with larger value spaces, making it suitable for real-time applications or resource-constrained environments. The approach is particularly useful in low-density parity-check (LDPC) codes, belief propagation algorithms, or other graph-based optimization problems where efficiency is critical. The constrained value set enhances convergence speed and reduces memory requirements, providing a balanced trade-off between performance and accuracy.
10. The method of claim 1 , wherein the one or more adjustments to the one or more beamforming weights associated with the first identified link to reduce interference among the network nodes of the multi-hop wireless network are determined by applying a belief-propagation algorithm to a portion of the factor-graph representation associated with at least the first identified link.
This invention relates to optimizing beamforming weights in a multi-hop wireless network to reduce interference among network nodes. The method involves adjusting beamforming weights for a first identified link by applying a belief-propagation algorithm to a factor-graph representation associated with that link. The factor-graph representation models the relationships between nodes and links in the network, capturing dependencies and interference patterns. The belief-propagation algorithm processes this graph to determine optimal adjustments to the beamforming weights, minimizing interference while maintaining communication quality. The approach leverages distributed computation, allowing nodes to exchange information and iteratively refine their beamforming strategies. This technique is particularly useful in dense wireless networks where interference management is critical for reliable data transmission. The method may also be extended to other links in the network by applying similar adjustments based on their respective factor-graph representations. The solution improves network performance by dynamically adapting beamforming weights to changing interference conditions.
11. The method of claim 10 , wherein the determined adjustments optimize a sum-capacity of the multi-hop wireless network.
A method for optimizing the sum-capacity of a multi-hop wireless network involves adjusting transmission parameters to enhance overall network performance. The network consists of multiple nodes that relay data across multiple hops, where each node may act as both a transmitter and a receiver. The method determines adjustments to transmission parameters, such as power levels, modulation schemes, or scheduling, based on network conditions. These adjustments are designed to maximize the total data throughput across the entire network by balancing individual link capacities and minimizing interference. The optimization process considers factors like channel quality, node locations, and traffic demands to ensure efficient data flow. By dynamically adapting these parameters, the method improves the sum-capacity, which refers to the aggregate data rate achievable by all nodes in the network. This approach is particularly useful in scenarios where network resources are limited, and efficient utilization is critical for maintaining high performance. The method may also incorporate feedback mechanisms to continuously monitor and refine adjustments in response to changing network conditions.
12. The method of claim 10 , wherein the determined adjustments optimize a sum-capacity of a portion of the multi-hop wireless network associated with the portion of the factor-graph representation.
This invention relates to optimizing the performance of a multi-hop wireless network by adjusting network parameters to maximize the sum-capacity of a portion of the network. The method involves analyzing a factor-graph representation of the network, which models the relationships between nodes and links in the network. The factor-graph representation includes variables representing network parameters such as transmission power, channel allocation, or routing paths, and factors representing constraints or objectives like interference, capacity, or energy efficiency. The method determines adjustments to these parameters by solving an optimization problem that maximizes the sum-capacity of a specific portion of the network, which is a measure of the total data-carrying capacity of that portion. The adjustments are applied to the network to improve its overall performance. The optimization may involve techniques such as linear programming, convex optimization, or iterative algorithms to find the optimal parameter settings. The method is particularly useful in scenarios where network resources are limited, and efficient use of capacity is critical, such as in dense wireless networks or IoT deployments.
13. The method of claim 10 , wherein the determined adjustments comprise adjusting a signal phase shift of the identified link.
This invention relates to wireless communication systems, specifically to methods for optimizing signal transmission in a network with multiple nodes. The problem addressed is the degradation of signal quality due to interference and multipath effects, which can reduce data throughput and reliability. The invention provides a method to dynamically adjust signal parameters to mitigate these issues. The method involves identifying a communication link between nodes that is experiencing signal degradation. This is done by analyzing signal quality metrics such as signal-to-noise ratio (SNR) or bit error rate (BER). Once a problematic link is identified, the method determines adjustments to optimize signal transmission. These adjustments include modifying the phase shift of the signal to improve alignment with the receiver, reducing interference and enhancing signal integrity. The phase shift adjustment is calculated based on real-time measurements of the link's performance. The system may use feedback from the receiver or predictive algorithms to determine the optimal phase shift. By dynamically adjusting the phase, the method compensates for environmental factors like multipath fading and interference, ensuring more reliable data transmission. This approach is particularly useful in dense wireless networks where interference is a significant challenge. The result is improved signal quality, higher data rates, and more stable connections.
14. The method of claim 10 , wherein the determined adjustments comprise a time division multiplexing scheme.
A method for optimizing communication in a wireless network involves adjusting transmission parameters to improve performance. The method includes monitoring network conditions, such as signal interference, latency, or data throughput, and dynamically adjusting transmission parameters based on the monitored conditions. These adjustments may include modifying transmission power, frequency allocation, or modulation schemes to enhance signal quality and reduce interference. The method further involves implementing a time division multiplexing (TDM) scheme, where multiple signals are transmitted over the same frequency channel but at different time intervals. This TDM scheme allows multiple devices to share the same communication medium efficiently, reducing collisions and improving overall network throughput. The adjustments are applied in real-time to adapt to changing network conditions, ensuring reliable and efficient data transmission. The method may also include feedback mechanisms to refine the adjustments based on performance metrics, such as error rates or latency, to further optimize communication. This approach is particularly useful in dense wireless environments where interference and resource contention are significant challenges.
15. The method of claim 10 , wherein the determined adjustments exclude one or more micro-routes on the multi-hop wireless network.
This invention relates to optimizing communication in multi-hop wireless networks by dynamically adjusting routing paths to improve performance. The problem addressed is the inefficiency and unreliability of data transmission in such networks due to suboptimal routing decisions, which can lead to packet loss, increased latency, or excessive energy consumption. The method involves analyzing network conditions, such as link quality, congestion, or node availability, to determine adjustments to the routing paths. These adjustments may include excluding certain micro-routes—short, direct connections between nodes—from the network's routing decisions. By avoiding problematic micro-routes, the system can enhance overall network performance, reduce packet loss, and improve energy efficiency. The adjustments are based on real-time or near-real-time data, allowing the network to adapt to changing conditions. The method may also consider historical performance data to predict and prevent future routing issues. Additionally, the system may prioritize certain routes over others based on predefined criteria, such as bandwidth requirements or latency constraints. This approach is particularly useful in environments where network conditions are dynamic, such as in IoT deployments, mesh networks, or mobile ad-hoc networks. By dynamically excluding unreliable micro-routes, the system ensures more stable and efficient data transmission across the network.
16. The method of claim 10 , further comprising: sending at least one of the determined adjustments to at least one of the network nodes of the multi-hop wireless network.
This invention relates to optimizing network performance in a multi-hop wireless network, where data is relayed through multiple nodes to reach its destination. A key challenge in such networks is maintaining efficient communication paths despite dynamic conditions like node mobility, interference, or changing traffic demands. The invention addresses this by dynamically adjusting network parameters to improve performance. The method involves monitoring network conditions, such as link quality, node availability, or traffic load, to identify suboptimal paths or nodes. Based on this analysis, adjustments are determined to optimize routing, transmission power, or other parameters. These adjustments are then sent to the relevant network nodes to implement changes. The adjustments may include rerouting data through alternative paths, modifying transmission power levels, or adjusting scheduling to reduce congestion. The goal is to enhance reliability, reduce latency, and improve overall network efficiency. The method may also involve predicting future network conditions to proactively adjust parameters before performance degrades. This predictive approach helps maintain stable communication even in highly dynamic environments. The adjustments are distributed to the affected nodes, ensuring that the entire network adapts in a coordinated manner. This dynamic optimization is particularly useful in scenarios like mesh networks, IoT deployments, or military communications where static configurations are impractical.
17. A system comprising: one or more processors; and a memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to: access an interference map indicating interference among network nodes of the multi-hop wireless network, wherein each of the network nodes of the multi-hop wireless network comprises one or more sectors that each comprise an array of beamforming antennae; identify one or more links between network nodes of the multi-hop wireless network, wherein each link is associated with a transmitting (TX) beamforming weight variable and a receiving (RX) beamforming weight variable; generate a factor-graph representation of the multi-hop wireless network, wherein the factor-graph representation comprises a first set of vertices corresponding to a second set of vertices, each vertex pair being associated with an identified link, and wherein, for each vertex pair, the vertex in the first set is a variable node representing the pair of beamforming weight variables associated with the identified link and each vertex in the second set is a function node representing a capacity equation associated with the identified link; and determine, for at least a first identified link, one or more adjustments to one or more beamforming weights associated with the first identified link to reduce interference among the network nodes of the multi-hop wireless network, wherein the one or more adjustments are determined based at least in part on one or more messages passed from a first variable node representing the pair of beamforming weight variables associated with the first identified link to a first function node representing a capacity equation associated with the first identified link, and wherein the one or more messages are derived at least in part based on one or more messages previously passed from the first variable node to the first function node.
The system optimizes beamforming in multi-hop wireless networks to reduce interference. In such networks, nodes with sectorized beamforming antenna arrays communicate via links, each defined by transmit (TX) and receive (RX) beamforming weights. Interference among nodes degrades performance, requiring dynamic adjustment of beamforming weights. The system accesses an interference map detailing node interactions and identifies links between nodes. It constructs a factor-graph representation of the network, where vertices form pairs: variable nodes represent TX/RX weight pairs for each link, and function nodes encode capacity equations governing link performance. Messages are exchanged between these nodes to iteratively refine beamforming weights. For a given link, adjustments are derived by passing messages from its variable node to its function node, incorporating prior message exchanges to refine weight values. This iterative process minimizes interference while maximizing network capacity. The approach leverages graph-based optimization to dynamically adapt beamforming in complex, multi-hop environments.
18. One or more computer-readable non-transitory storage media embodying software that is operable when executed to: access an interference map indicating interference among network nodes of the multi-hop wireless network, wherein each of the network nodes of the multi-hop wireless network comprises one or more sectors that each comprise an array of beamforming antennae; identify one or more links between network nodes of the multi-hop wireless network, wherein each link is associated with a transmitting (TX) beamforming weight variable and a receiving (RX) beamforming weight variable; generate a factor-graph representation of the multi-hop wireless network, wherein the factor-graph representation comprises a first set of vertices corresponding to a second set of vertices, each vertex pair being associated with an identified link, and wherein, for each vertex pair, the vertex in the first set is a variable node representing the pair of beamforming weight variables associated with the identified link and each vertex in the second set is a function node representing a capacity equation associated with the identified link; and determine, for at least a first identified link, one or more adjustments to one or more beamforming weights associated with the first identified link to reduce interference among the network nodes of the multi-hop wireless network, wherein the one or more adjustments are determined based at least in part on one or more messages passed from a first variable node representing the pair of beamforming weight variables associated with the first identified link to a first function node representing a capacity equation associated with the first identified link, and wherein the one or more messages are derived at least in part based on one or more messages previously passed from the first variable node to the first function node.
This invention relates to optimizing beamforming in multi-hop wireless networks to reduce interference among network nodes. The system addresses the challenge of managing interference in networks where nodes communicate via directional beams, particularly in environments with dynamic conditions or dense deployments. Each network node includes one or more sectors, each equipped with an array of beamforming antennae. The system accesses an interference map that details interference patterns among nodes and identifies links between them, each link associated with transmit (TX) and receive (RX) beamforming weight variables. The system generates a factor-graph representation of the network, where vertices form two sets: variable nodes representing beamforming weight pairs for each link and function nodes representing capacity equations for those links. Messages are exchanged between these nodes to iteratively refine beamforming weights. Specifically, for a given link, adjustments to its beamforming weights are determined by passing messages from the variable node (representing the link's weights) to the function node (representing its capacity equation). These messages incorporate prior message exchanges to iteratively optimize the weights, reducing interference across the network. The approach leverages factor-graph techniques to model and solve the beamforming optimization problem efficiently.
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December 29, 2020
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